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11 May 1987 Qualitative Constraint Reasoning For Image Understanding
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Abstract
Military planners and analysts are exceedingly concerned with increasing the effectiveness of command and control (C2) processes for battlefield management (BM). A variety of technical approaches have been taken in this effort. These approaches are intended to support and assist commanders in situation assessment, course of action generation and evaluation, and other C2 decision-making tasks. A specific task within this technology support includes the ability to effectively gather information concerning opposing forces and plan/replan tactical maneuvers. Much of the information that is gathered is image-derived, along with collateral data supporting this visual imagery. In this paper, we intend to describe a process called qualitative constraint reasoning (QCR) which is being developed as a mechanism for reasoning in the mid to high level vision domain. The essential element of QCR is the abstraction process. One of the factors that is unique to QCR is the level at which the abstraction process occurs relative to the problem domain. The computational mechanisms used in QCR belong to a general class of problem called the consistent labeling problem. The success of QCR is its ability to abstract out from a visual domain a structure appropriate for applying the labeling procedure. An example will be given that will exemplify the abstraction process for a battlefield management application. Exploratory activities are underway for investigating the suitability of QCR approach for the battlefield scenario. Further research is required to investigate the utility of QCR in a more complex battlefield environment.
© (1987) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John L. Perry "Qualitative Constraint Reasoning For Image Understanding", Proc. SPIE 0786, Applications of Artificial Intelligence V, (11 May 1987); https://doi.org/10.1117/12.940617
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